CN112668810A - Method for planning and configuring group-buying fresh warehouse of community according to delivery time window - Google Patents
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Abstract
The invention discloses a method for planning and configuring a community group-buying fresh warehouse according to a delivery time window, wherein the method for constructing an evaluation matrix comprises the following steps: s1, constructing a trapezoidal matrix, and describing evaluation information by adopting a trapezoidal fuzzy number; s2, normalizing the matrix, keeping the benefit criterion unchanged, and normalizing the initial evaluation matrix by using an inverse operation on the cost criterion; s3, determining the optimal and worst schemes, and respectively calculating the optimal and worst schemes under each criterion; s4, calculating the distance from each scheme to the relatively optimal and worst ideal schemes, and calculating the distance from each scheme to the relatively optimal and worst ideal schemes by utilizing a Hamming distance formula of fixed trapezoidal fuzzy numbers, so that the problem of reasonable configuration of the community group purchase fresh-keeping warehouse based on the requirement of a distribution time window is solved, the requirement of a delivery time window can be met by skills, the whole community group purchase warehouse can be made to have the lowest matching cost, the community group purchase efficiency is improved, and the community group purchase is convenient to popularize better.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a planning and configuration method of a community group-buying fresh-keeping warehouse according to a delivery time window.
Background
The community group purchase is developed rapidly in recent years as a novel online shopping mode, along with the continuous expansion of market scale, the community group purchase is upgraded on the traditional O2O mode, the flow is obtained mainly by depending on social platforms such as WeChat and QQ, deep link is carried out on users in a community and an internet platform through a group growth stable KOL, and the high-frequency and fast-disappearing rigidity requirements of community users are met through a light asset operation mode of 'single product money explosion + pre-sale';
for community group purchase, a warehouse is used as a node of a logistics network and is connected with an upstream supply point and a downstream demand point, the whole community group purchase system plays a role of starting and stopping, and the proper selection of the position of the warehouse directly relates to the whole distribution efficiency, the logistics cost and the customer satisfaction;
undoubtedly, the correct warehouse configuration can effectual control risk to community group purchase, reduce cost, promote customer satisfaction, community group purchase business is mostly giving birth to xian, all be higher to the requirement of distribution timeliness, for example, vegetables, generally pick four or five o' clock in the morning, in case the delivery exceeds 6 hours, will lead to the dish quality to descend, thereby bring bad experience for the terminal customer, the configuration of the fresh warehouse especially needs reasonable configuration based on above reason, the delivery time window restraint of fresh goods is more harsh.
Disclosure of Invention
The invention provides a method for planning and configuring a group-buying fresh food warehouse of a community according to a delivery time window, which can effectively solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the method for planning and configuring the group purchase fresh warehouse of the community according to the delivery time window comprises the following steps:
s1, loading the city map by using a space-time calculation engine;
s2, importing detailed positions of the bouquet, and importing the specific positions of the bouquet into a space-time calculation engine;
s3, importing fresh order data, and importing the fresh order data of each group into a space-time calculation engine;
s4, generating a geographic grid, analyzing the group growth data and the fresh order data of each grid through spatial region statistics, and realizing association;
s5, classifying the warehouses, and defining the storage capacity, the average sorting efficiency and the loading efficiency of the warehouses of each grade; defining the operation cost of each warehouse;
s6, classifying the transport vehicles, and defining the loading capacity and the average freight of each type of vehicle;
s7, defining decision variables, warehouse quantity and level, and vehicle quantity and type;
s8, defining an objective function as the lowest warehousing and transportation cost;
s9, solving the model, trying a linear, nonlinear and neural network planning model, and trying to find the TOP10 of the optimal solution;
and S10, loading the number of fresh warehouses solved by the model and the grids in the warehouses to a space-time calculation engine, and selecting an optimal scheme by expert review and by using TOPSIS.
According to the technical scheme, the constraint conditions in S8 are defined to be that the fresh food can be delivered on time according to the delivery time window of the fresh food, the time window constraint includes a start time constraint and an end time constraint of the delivery, and the model initial parameters assume that each city has 20 fresh food warehouses.
According to the technical scheme, in the step S10, the optimal solution is selected, the expert interview method is used to obtain the evaluation value of each distribution center candidate solution under each criterion, in order to reasonably depict the fuzziness of evaluators, trapezoidal fuzzy numbers are used to describe the evaluation information, and an evaluation matrix is constructed.
According to the technical scheme, the construction of the evaluation matrix comprises the following steps:
s1, constructing a trapezoidal matrix, and describing evaluation information by adopting a trapezoidal fuzzy number;
s2, normalizing the matrix, keeping the benefit criterion unchanged, and normalizing the initial evaluation matrix by using an inverse operation on the cost criterion;
s3, determining the optimal and worst schemes, and respectively calculating the optimal and worst schemes under each criterion;
and S4, calculating the distance from each scheme to the relatively optimal and worst ideal schemes, and calculating the distance from each scheme to the relatively optimal and worst ideal schemes by utilizing a Hamming distance formula of fixed trapezoidal fuzzy numbers.
According to the above technical solution, the distances from each solution to the relatively optimal and worst ideal solutions in S4 are calculated as follows:
evaluation index
；
And the optimal distribution center alternative schemes sort each distribution center alternative scheme according to the comprehensive evaluation index of each scheme obtained in the formula, wherein the larger the comprehensive evaluation index is, the shorter the distance between the scheme and the optimal ideal scheme is, so that the optimal scheme is also, and the most satisfactory warehouse position is finally selected.
Compared with the prior art, the invention has the beneficial effects that: the invention has scientific and reasonable structure and safe and convenient use, solves the problem of reasonable configuration of the fresh group purchasing warehouse of the community based on the requirement of a delivery time window, can meet the requirement of a delivery time window by skill, can ensure that the whole group purchasing warehouse of the community has the lowest matching cost, improves the efficiency of group purchasing of the community, and is convenient for better popularization of group purchasing of the community.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a schematic diagram of the process steps of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example (b): as shown in fig. 1, the technical solution provided by the present invention is a method for configuring a group buying fresh food warehouse of a community according to a distribution time window plan, comprising the following steps:
s1, loading the city map by using a space-time calculation engine;
s2, importing detailed positions of the bouquet, and importing the specific positions of the bouquet into a space-time calculation engine;
s3, importing fresh order data, and importing the fresh order data of each group into a space-time calculation engine;
s4, generating a geographic grid, analyzing the group growth data and the fresh order data of each grid through spatial region statistics, and realizing association;
s5, classifying the warehouses, and defining the storage capacity, the average sorting efficiency and the loading efficiency of the warehouses of each grade; defining the operation cost of each warehouse;
s6, classifying the transport vehicles, and defining the loading capacity and the average freight of each type of vehicle;
s7, defining decision variables, warehouse quantity and level, and vehicle quantity and type;
s8, defining an objective function as the lowest warehousing and transportation cost;
s9, solving the model, trying a linear, nonlinear and neural network planning model, and trying to find the TOP10 of the optimal solution;
and S10, loading the number of fresh warehouses solved by the model and the grids in the warehouses to a space-time calculation engine, and selecting an optimal scheme by expert review and by using TOPSIS.
According to the above technical solution, the constraint conditions in S8 are defined to be able to deliver the fresh food product on time according to the delivery time window of the fresh food product, the time window constraint includes the start time constraint and the end time constraint of the delivery, and the model initial parameters assume that there are 20 fresh food warehouses in each city.
According to the technical scheme, in the step S10, the optimal scheme is selected, the expert interview method is used for obtaining the evaluation value of each distribution center alternative scheme under each criterion, in order to reasonably depict the fuzziness of an evaluator, the trapezoidal fuzzy number is used for describing evaluation information, and an evaluation matrix is constructed.
According to the technical scheme, the method for constructing the evaluation matrix comprises the following steps:
s1, constructing a trapezoidal matrix, and describing evaluation information by adopting a trapezoidal fuzzy number;
s2, normalizing the matrix, keeping the benefit criterion unchanged, and normalizing the initial evaluation matrix by using an inverse operation on the cost criterion;
s3, determining the optimal and worst schemes, and respectively calculating the optimal and worst schemes under each criterion;
and S4, calculating the distance from each scheme to the relatively optimal and worst ideal schemes, and calculating the distance from each scheme to the relatively optimal and worst ideal schemes by utilizing a Hamming distance formula of fixed trapezoidal fuzzy numbers.
According to the above technical solution, the distances from each solution to the relatively optimal and worst ideal solutions are calculated in S4 as follows:
evaluation index
；
And the optimal distribution center alternative schemes sort each distribution center alternative scheme according to the comprehensive evaluation index of each scheme obtained in the formula, wherein the larger the comprehensive evaluation index is, the shorter the distance between the scheme and the optimal ideal scheme is, so that the optimal scheme is also, and the most satisfactory warehouse position is finally selected.
Compared with the prior art, the invention has the beneficial effects that: the invention has scientific and reasonable structure and safe and convenient use, solves the problem of reasonable configuration of the fresh group purchasing warehouse of the community based on the requirement of a delivery time window, can meet the requirement of a delivery time window by skill, can ensure that the whole group purchasing warehouse of the community has the lowest matching cost, improves the efficiency of group purchasing of the community, and is convenient for better popularization of group purchasing of the community.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The method for planning and configuring the group-buying fresh food warehouse of the community according to the delivery time window is characterized by comprising the following steps: the method comprises the following steps:
s1, loading the city map by using a space-time calculation engine;
s2, importing detailed positions of the bouquet, and importing the specific positions of the bouquet into a space-time calculation engine;
s3, importing fresh order data, and importing the fresh order data of each group into a space-time calculation engine;
s4, generating a geographic grid, analyzing the group growth data and the fresh order data of each grid through spatial region statistics, and realizing association;
s5, classifying the warehouses, and defining the storage capacity, the average sorting efficiency and the loading efficiency of the warehouses of each grade; defining the operation cost of each warehouse;
s6, classifying the transport vehicles, and defining the loading capacity and the average freight of each type of vehicle;
s7, defining decision variables, warehouse quantity and level, and vehicle quantity and type;
s8, defining an objective function as the lowest warehousing and transportation cost;
s9, solving the model, trying a linear, nonlinear and neural network planning model, and trying to find the TOP10 of the optimal solution;
and S10, loading the number of fresh warehouses solved by the model and the grids in the warehouses to a space-time calculation engine, and selecting an optimal scheme by expert review and by using TOPSIS.
2. The method as claimed in claim 1, wherein the constraints of S8 are defined as being able to deliver the fresh produce on time according to a delivery time window, the time window constraints include a start time constraint and an end time constraint of the delivery, and the model initial parameters assume that there are 20 fresh produce warehouses per city.
3. The method for planning and configuring the community group purchase fresh food warehouse according to the distribution time window as claimed in claim 1, wherein in the step S10, an optimal solution is selected, an expert interview method is used to obtain an evaluation value of each distribution center alternative solution under each criterion, a trapezoidal fuzzy number is used to describe evaluation information in order to reasonably depict the fuzziness of evaluators, and an evaluation matrix is constructed.
4. The method for planning and configuring the community group purchase fresh food warehouse according to the delivery time window according to claim 3, wherein the step of constructing the evaluation matrix comprises the following steps:
s1, constructing a trapezoidal matrix, and describing evaluation information by adopting a trapezoidal fuzzy number;
s2, normalizing the matrix, keeping the benefit criterion unchanged, and normalizing the initial evaluation matrix by using an inverse operation on the cost criterion;
s3, determining the optimal and worst schemes, and respectively calculating the optimal and worst schemes under each criterion;
and S4, calculating the distance from each scheme to the relatively optimal and worst ideal schemes, and calculating the distance from each scheme to the relatively optimal and worst ideal schemes by utilizing a Hamming distance formula of fixed trapezoidal fuzzy numbers.
5. The method as claimed in claim 4, wherein the distances from each solution to the optimal and worst ideal solutions are calculated as follows in S4:
evaluation index
；
And the optimal distribution center alternative schemes sort each distribution center alternative scheme according to the comprehensive evaluation index of each scheme obtained in the formula, wherein the larger the comprehensive evaluation index is, the shorter the distance between the scheme and the optimal ideal scheme is, so that the optimal scheme is also, and the most satisfactory warehouse position is finally selected.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114620402A (en) * | 2022-03-17 | 2022-06-14 | 上海禹璨信息技术有限公司 | Information processing method, device, equipment and storage medium |
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2021
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114620402A (en) * | 2022-03-17 | 2022-06-14 | 上海禹璨信息技术有限公司 | Information processing method, device, equipment and storage medium |
| CN114620402B (en) * | 2022-03-17 | 2023-06-06 | 上海禹璨信息技术有限公司 | Information processing method, device, equipment and storage medium |
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